Visualization

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
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## ✔ tibble  3.1.8      ✔ dplyr   1.0.10
## ✔ tidyr   1.2.0      ✔ stringr 1.4.1 
## ✔ readr   2.1.2      ✔ forcats 0.5.2 
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

library(ggridges)

Load the Weather Data

weather_df = 
  rnoaa::meteo_pull_monitors(
    c("USW00094728", "USC00519397", "USS0023B17S"),
    var = c("PRCP", "TMIN", "TMAX"), 
    date_min = "2017-01-01",
    date_max = "2017-12-31") %>%
  mutate(
    name = recode(
      id, 
      USW00094728 = "CentralPark_NY", 
      USC00519397 = "Waikiki_HA",
      USS0023B17S = "Waterhole_WA"),
    tmin = tmin / 10,
    tmax = tmax / 10) %>%
  select(name, id, everything())

## Registered S3 method overwritten by 'hoardr':
##   method           from
##   print.cache_info httr

## using cached file: ~/Library/Caches/R/noaa_ghcnd/USW00094728.dly

## date created (size, mb): 2022-09-03 15:53:33 (8.394)

## file min/max dates: 1869-01-01 / 2022-09-30

## using cached file: ~/Library/Caches/R/noaa_ghcnd/USC00519397.dly

## date created (size, mb): 2022-09-03 15:53:37 (1.697)

## file min/max dates: 1965-01-01 / 2020-02-29

## using cached file: ~/Library/Caches/R/noaa_ghcnd/USS0023B17S.dly

## date created (size, mb): 2022-09-03 15:53:39 (0.949)

## file min/max dates: 1999-09-01 / 2022-09-30

weather_df

## # A tibble: 1,095 × 6
##    name           id          date        prcp  tmax  tmin
##    <chr>          <chr>       <date>     <dbl> <dbl> <dbl>
##  1 CentralPark_NY USW00094728 2017-01-01     0   8.9   4.4
##  2 CentralPark_NY USW00094728 2017-01-02    53   5     2.8
##  3 CentralPark_NY USW00094728 2017-01-03   147   6.1   3.9
##  4 CentralPark_NY USW00094728 2017-01-04     0  11.1   1.1
##  5 CentralPark_NY USW00094728 2017-01-05     0   1.1  -2.7
##  6 CentralPark_NY USW00094728 2017-01-06    13   0.6  -3.8
##  7 CentralPark_NY USW00094728 2017-01-07    81  -3.2  -6.6
##  8 CentralPark_NY USW00094728 2017-01-08     0  -3.8  -8.8
##  9 CentralPark_NY USW00094728 2017-01-09     0  -4.9  -9.9
## 10 CentralPark_NY USW00094728 2017-01-10     0   7.8  -6  
## # … with 1,085 more rows

What the r chunk did:

searching online for the data we want
- rnoaa package allows us to read public data online instead of reading excel, csv files
- meteo_pull_monitors(c()) pull the monitors we want, in this case we are getting weather data from “USW”, “USC”, and “USS” stations
- var = c() bring the variables we want
- data_min and data_mx are used to set the starting and ending date we are interested
data manipulation through mutate()
- name = recode is used to rename variables
- select() is similiar to relocate, it’s used to manipulate the column order

Scatter Plots

ggplot(weather_df, aes(x = tmin, y = tmax)) + 
  geom_point()

## Warning: Removed 15 rows containing missing values (geom_point).

Typically, when we use ggplot

choose the data frame
aes()defining aesthetic mappings
choose the geomatrics

New approach, same graph.

The code below could be used instead to produce the same figure. Using this style can be helpful if you want to do some pre-processing before making your plot but don’t want to save the intermediate data.

weather_df %>%
  ggplot(aes(x = tmin, y = tmax)) + 
  geom_point()

## Warning: Removed 15 rows containing missing values (geom_point).

Also

We can also save the output of ggplot() to an object that can be printed and modified later.

plot_weather = 
  weather_df %>%
  ggplot(aes(x = tmin, y = tmax)) 

plot_weather + geom_point()

## Warning: Removed 15 rows containing missing values (geom_point).

Advanced Scatterplot

`color` and `geom_smooth`

ggplot(weather_df, aes(x = tmin, y = tmax, color = name)) + 
  geom_point() + 
  geom_smooth(se = FALSE)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## Warning: Removed 15 rows containing non-finite values (stat_smooth).

## Warning: Removed 15 rows containing missing values (geom_point).

How did we make it fancy: - color = cat_var color by catagetorical variables - geom_smooth additional geometry that can add to the plot which generate a smooth trajectery for the data

ggplot(weather_df, aes(x = tmin, y = tmax)) + 
  geom_point(aes(color = name)) + 
  geom_smooth()

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## Warning: Removed 15 rows containing non-finite values (stat_smooth).

## Warning: Removed 15 rows containing missing values (geom_point).

In this r chunk, colors only apply for the geom_point. Color is not defined in the general aesthetic mappings, so we only generated one smooth line through geom_smooth.

`alpha` in `geom_print`

ggplot(weather_df, aes(x = tmin, y = tmax, color = name)) + 
  geom_point(alpha = 0.5) + 
  geom_smooth(se = FALSE) +
  facet_grid(. ~ name)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## Warning: Removed 15 rows containing non-finite values (stat_smooth).

## Warning: Removed 15 rows containing missing values (geom_point).

What we added:

facet_grid(. ~ col_var) the . means the row variable is not defined here. The column variable is defined (~) as “name”.
alpha = 0.5 in geom_point changes the transperancy level

`alpha` in global/general setting

ggplot(weather_df, aes(x = tmin, y = tmax, alpha = tmin, color = name)) + 
  geom_point() + 
  geom_smooth(se = FALSE) +
  facet_grid(. ~ name)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## Warning: Removed 15 rows containing non-finite values (stat_smooth).

## Warning: Removed 15 rows containing missing values (geom_point).

What we changed:

alpha = tmin in aes() gives a color gradient in the plot. the smaller “tmin” has more transparent color

New plot: Sizing the data point based on a variable

We sized the data point based on the “prcp” variable, where the higher “prcp” the bigger the data point showed on graph, via geom_point(aes(size = prcp)).

weather_df %>% 
  ggplot(aes(x = date, y = tmax, color = name)) + 
  geom_point(aes(size = prcp), alpha = 0.5) +
  geom_smooth(se = FALSE) +
  facet_grid(. ~name)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## Warning: Removed 3 rows containing non-finite values (stat_smooth).

## Warning: Removed 3 rows containing missing values (geom_point).

Some Notes

We can have whatever geoms we want. The number of geoms is not limited.

Having one gemo.

weather_df %>% 
  ggplot(aes(x = tmin, y = tmax, color = name)) + 
  geom_smooth(se = FALSE)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## Warning: Removed 15 rows containing non-finite values (stat_smooth).

Having two gemos.

weather_df %>% 
  ggplot(aes(x = tmin, y = tmax, color = name)) + 
  geom_point() + 
  geom_smooth(se = FALSE)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## Warning: Removed 15 rows containing non-finite values (stat_smooth).

## Warning: Removed 15 rows containing missing values (geom_point).

We can have a neat geom.

`geom_hex()`

The geom_hex() shows as hexagens.

weather_df %>% 
  ggplot(aes(x = tmin, y = tmax)) + 
  geom_hex()

## Warning: Removed 15 rows containing non-finite values (stat_binhex).

Or `geom_bin2d()`

The geom_bin2d() shows as squares.

weather_df %>% 
  ggplot(aes(x = tmin, y = tmax)) + 
  geom_bin2d()

## Warning: Removed 15 rows containing non-finite values (stat_bin2d).

Or something looks like a elevation map

weather_df %>% 
  ggplot(aes(x = tmin, y = tmax)) + 
  geom_density2d() + 
  geom_point(alpha = 0.3)

## Warning: Removed 15 rows containing non-finite values (stat_density2d).

## Warning: Removed 15 rows containing missing values (geom_point).

Univariate Plots

Histograms

weather_df %>% 
  ggplot(aes(x = tmin)) +
  geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 15 rows containing non-finite values (stat_bin).

Add color in histogram.

weather_df %>% 
  ggplot(aes(x = tmin, color = name)) +
  geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 15 rows containing non-finite values (stat_bin).

This graph looks weired, so we want to change:

fill the color by using fill = name instead of color = name
change the histograms’ positions and make them aside to each other, via position = "dodge"

weather_df %>% 
  ggplot(aes(x = tmin, fill = name)) +
  geom_histogram(position = "dodge")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 15 rows containing non-finite values (stat_bin).

We can also change the position by separating the graph into 3 graphs, where each graph contains histograms for one location only.

weather_df %>% 
  ggplot(aes(x = tmin, fill = name)) +
  geom_histogram() +
  facet_grid(. ~ name)

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 15 rows containing non-finite values (stat_bin).

New geometry `geom_density`

geom_density kind of smooth out the edges of the histograms. It shows the overall shape of the distribution, but we will lose those little bumps.

weather_df %>% 
  ggplot(aes(x = tmin, fill = name)) +
  geom_density(alpha = 0.4)

## Warning: Removed 15 rows containing non-finite values (stat_density).

Boxplots

weather_df %>% 
  ggplot(aes(y = tmin)) +
  geom_boxplot()

## Warning: Removed 15 rows containing non-finite values (stat_boxplot).

We stratify the data points based on locations (“name”).

weather_df %>% 
  ggplot(aes(x = name, y = tmin)) +
  geom_boxplot()

## Warning: Removed 15 rows containing non-finite values (stat_boxplot).

Trendy Plots

Violin Plot

weather_df %>% 
  ggplot(aes(x = name, y = tmin, fill = name)) +
  geom_violin(alpha = 0.5) +
  stat_summary(fun = "median")

## Warning: Removed 15 rows containing non-finite values (stat_ydensity).

## Warning: Removed 15 rows containing non-finite values (stat_summary).

## Warning: Removed 3 rows containing missing values (geom_segment).

Turn your head for 90 degrees, on the side, the violin plot show you the distribution.

We can add statistic features/functions in the plot by using stat_summary(fun = "median"). In this case we added one of the summary statistics functions, the median.

Ridge plot - the most popular plot of 2017

weather_df %>% 
  ggplot(aes(x = tmin, y = name, fill = name)) +
  geom_density_ridges(alpha = 0.5)

## Picking joint bandwidth of 1.67

## Warning: Removed 15 rows containing non-finite values (stat_density_ridges).

Ridge plots show you the distributions.

Visualization

Part I

Tianyou Wang

2022-10-11

Load the Weather Data

Scatter Plots

New approach, same graph.

Also

Advanced Scatterplot

`color` and `geom_smooth`

`alpha` in `geom_print`

`alpha` in global/general setting

New plot: Sizing the data point based on a variable

Some Notes

We can have whatever geoms we want. The number of geoms is not limited.

Having one gemo.

Having two gemos.

We can have a neat geom.

`geom_hex()`

Or `geom_bin2d()`

Or something looks like a elevation map

Univariate Plots

Histograms

New geometry `geom_density`

Boxplots

Trendy Plots

Violin Plot

Ridge plot - the most popular plot of 2017

Visualization

Part I

Tianyou Wang

2022-10-11

Load the Weather Data

Scatter Plots

New approach, same graph.

Also

Advanced Scatterplot

color and geom_smooth

alpha in geom_print

alpha in global/general setting

New plot: Sizing the data point based on a variable

Some Notes

We can have whatever geoms we want. The number of geoms is not limited.

Having one gemo.

Having two gemos.

We can have a neat geom.

geom_hex()

Or geom_bin2d()

Or something looks like a elevation map

Univariate Plots

Histograms

New geometry geom_density

Boxplots

Trendy Plots

Violin Plot

Ridge plot - the most popular plot of 2017

`color` and `geom_smooth`

`alpha` in `geom_print`

`alpha` in global/general setting

`geom_hex()`

Or `geom_bin2d()`

New geometry `geom_density`